Impact of Climate Variability and Weather Extremes on Terrestrial and Aquatic Systems

Abstract

Inland freshwater ecosystems have been experiencing rapid and notable transformations in direct response to both climate shifts and human-induced stressors during recent decades. Land Surface Models (LSMs) play a vital role in providing information on various aspects of the Earth's surface, including hydrological processes, biophysical characteristics, and biogeochemical dynamics. Over time, LSMs have evolved from simplified depictions of land surface biophysics to incorporate a diverse range of interrelated processes, including modified vegetation dynamics, groundwater interactions, and hydrological processes. Given the profound impact of hydrological processes on a multitude of biophysical and biogeochemical mechanisms within the Earth system, the inclusion of lakes and human-made reservoirs in land surface models (LSMs) is currently in its nascent stages. To enhance our understanding of the impact of hydrometeorological changes on water quality and to address the limitations in representing inland water bodies within Land Surface Models (LSMs), three research studies were conducted. These studies employed the Noah land surface model (Noah-MP) with multiple parameterization options and the General Lake Model (GLM); models were used individually and in combination. In the first study, the influence of vegetation dynamics is thoroughly examined, with specific attention given to six different configurations of leaf area index (LAI) and vegetation fraction (FVEG) and the impact on streamflow. The main objective was to evaluate how these configurations impact the representation of eco-hydrological processes in a semi-arid region primarily characterized by grasslands. Additionally, the study aims to analyze the performance of streamflow simulation and the capability to predict drought conditions at scales beyond the site level. The results indicate that the incoming net radiation plays a crucial role in constraining the total evaporation process in energy-limited environments. It was observed that an overestimation of latent heat (LE) led to an underestimation of streamflow. Furthermore, the analysis indicated that all of the newer version (Noah-MP 4.0.1) vegetation physics demonstrated a higher degree of accuracy in reproducing spatial patterns of drought compared to the older version 3.6. These findings are then used to select the optimal Noah-MP model configuration which is used in the last chapter. The second study focused on quantifying the impact of atmospheric stilling on polymictic reservoirs, aiming to enhance predictions of the phytoplankton community composition. High-resolution temporal in-situ data from Marion Reservoir in Kansas were employed to identify the biotic and abiotic factors that influence the composition and dynamics of phytoplankton in shallow reservoirs. The study revealed that a combination of rising air temperatures, calm weather conditions, and light penetration depth emerged as the primary drivers responsible for triggering algal blooms. Additionally, the internal nutrient loading during anoxic conditions was found to have a direct impact on the intensification of harmful cyanobacterial blooms (CyanoHABs). The last chapter then focuses on the integration of the General Lake Model (GLM) with a Noah-MP to improve the prediction of lake thermodynamic patterns, particularly in shallow lakes and reservoirs. Remarkably, the simulation of lake thermal dynamics, driven by the forcings from the North American Land Data Assimilation System-2 (NLDAS-2) and incorporating modeled surface runoff from Noah-MP, exhibited a capacity to reproduce reservoir thermal regimes that surpassed field measurements, albeit marginally. Overall, this dissertation offers a thorough assessment of the performance of the state-of-the-art land surface model, Noah-MP, providing valuable insights into the integration of the GLM within this framework at Marion Reservoir in Kansas.